Bibliographic record
Abstract
Case-cohort studies are attractive for studying rare diseases where obtaining additional expensive or hard-to-access data, such as genomic sequencing, from a subset of participants is infeasible for the entire study cohort. In analyzing such studies, individual data points must be appropriately weighted to account for the biased case/control sampling. The Cox proportional hazards model is a popular semi-parametric method for analyzing survival data that provides step function risk estimates. A parametric alternative is the casebase framework, which uses finite sampling of person-moments together with logistic regression to estimate fully parametric hazard functions and smooth-in-time absolute risk functions. Unlike the Cox model, where well-tested methods exist to adjust for complex sampling designs, the casebase framework-based methods have not yet implemented weighted methods. This thesis proposes a weighted casebase framework that provides unbiased coefficient estimates and robust standard error estimates. A simulation study compares the performance of weighted Cox and casebase models. The proposed weighted analytic framework is then applied to model how cell-free DNA methylation (data obtained with the cfMeDIP-seq technology) affects risk of breast cancer in a (case-cohort) subset of individuals in the Ontario Health Study (OHS). The weighted framework performs similarly to weighted Cox models, and both are sensitive to covariate distributions and the size of the sampling fraction
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".